A multiple imputation approach to the analysis of clustered interval-censored failure time data with the additive hazards model

Ling Chen, Jianguo Sun and Chengjie Xiong

Computational Statistics & Data Analysis, 2016, vol. 103, issue C, 242-249

Abstract: Clustered interval-censored failure time data can occur when the failure time of interest is collected from several clusters and known only within certain time intervals. Regression analysis of clustered interval-censored failure time data is discussed assuming that the data arise from the semiparametric additive hazards model. A multiple imputation approach is proposed for inference. A major advantage of the approach is its simplicity because it avoids estimating the correlation within clusters by implementing a resampling-based method. The presented approach can be easily implemented by using the existing software packages for right-censored failure time data. Extensive simulation studies are conducted, indicating that the proposed imputation approach performs well for practical situations. The proposed approach also performs well compared to the existing methods and can be more conveniently applied to various types of data representation. The proposed methodology is further demonstrated by applying it to a lymphatic filariasis study.

Keywords: Additive hazards model; Clustered interval-censored data; Multiple imputation; Within-cluster resampling (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S016794731630113X
Full text for ScienceDirect subscribers only.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:103:y:2016:i:c:p:242-249

DOI: 10.1016/j.csda.2016.05.011

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().